Update README.md

README.md

@@ -1,3 +1,4 @@
+---
 license: cc-by-nc-sa-4.0
 language:
 - en
@@ -40,3 +41,136 @@ configs:
     path: metadata/chain/M.jsonl
   - split: L
     path: metadata/chain/L.jsonl
+
+---
+
+# AudioSpan: Spanning the Duration and Depth of Audio Comprehension
+
+<p align="center">
+  <a href="https://arxiv.org/abs/ARXIV_ID"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=flat-square&logo=arxiv&logoColor=white" alt="arXiv"></a>
+  <a href="https://huggingface.co/datasets/holvan/AudioSpan"><img src="https://img.shields.io/badge/Dataset-AudioSpan-ffd21e?style=flat-square&logo=huggingface&logoColor=000" alt="Hugging Face Dataset"></a>
+  <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-3da639?style=flat-square" alt="License: CC BY-NC-SA 4.0"></a>
+</p>
+
+
+## Introduction
+
+**AudioSpan** is a benchmark for **long-form audio comprehension**, spanning
+diverse durations and cognitive depths.
+
+<p align="center">
+  <img src="figs/audio_span.png" alt="AudioSpan overview" width="100%">
+</p>
+
+
+Questions come from two complementary paths:
+
+- **Native QA**: questions drawn from the audio's natural content.
+- **Anchor QA**: questions built around acoustic anchors planted into the
+  audio.
+
+Each path is scored in its own mode:
+
+- **Accuracy**: multiple choice questions on native audio, scored by exact
+  match.
+- **Rubric**: open-ended questions on native audio, graded by rubric-based
+  LLM judges against criteria.
+- **Chain**: multiple-choice question chains on anchor audio; an answer is
+  credited only up to the first error in the chain.
+
+## Quick Start
+
+Download the release (~18 GB) and work from its root:
+
+```bash
+pip install -U huggingface_hub
+hf download holvan/AudioSpan --repo-type dataset --local-dir AudioSpan
+cd AudioSpan
+```
+
+### 1. Prepare Audio Data
+
+Native recordings and sound events ship as tar archives
+(`audio/audio_native.part*.tar`, `audio/audio_events.tar`; the native parts
+are concatenated automatically). From the release root (the directory with
+`prepare/` and `audio/`), verify them against `audio/CHECKSUMS.sha256` and
+unpack into `audio/`:
+
+```bash
+# accuracy / rubric: native recordings only
+python prepare/unpack_audio.py --buckets native
+# -> audio/native/*.flac
+
+# chain: also rebuild the anchor audio from the native recordings and the
+# exact edit recipes in metadata/media/anchor/anchor_manifest.jsonl
+# (needs only ffmpeg)
+python prepare/unpack_audio.py --buckets native,events
+python prepare/prepare_anchor.py
+# -> audio/anchor/*.flac
+```
+
+(`unpack_audio.py` resolves the release root from its own location; pass
+`--root` if you run it from elsewhere.)
+
+### 2. Prepare Inference Results
+
+The questions live in `metadata/{accuracy,rubric,chain}/{S,M,L}.jsonl`; each
+record carries the question, its `audio_path`, and (for multiple choice) the
+four options. Run your model over each record and write one answer per
+question — only `qa_id` and the model's output; ground truth stays in the
+metadata and is joined in by the scorer:
+
+**accuracy / chain** (multiple choice):
+
+```json
+{"qa_id": "S_EN_001_P", "answer": "D"}
+```
+
+**rubric** (open-ended):
+
+```json
+{"qa_id": "S_EN_001_P", "answer": "The narrator first says HDR at about 06:04 ..."}
+```
+
+- `answer` — the model's raw output; for multiple choice the scorer
+  extracts the option letter (A/B/C/D) from it.
+- Questions missing from your file count as wrong (or score zero).
+
+### 3. Run Evaluation
+
+```bash
+# accuracy + chain: stdlib-only, no dependencies
+python evaluate/score.py --mode accuracy --input your_accuracy.jsonl
+python evaluate/score.py --mode chain    --input your_chain.jsonl
+
+# rubric: needs an LLM judge (default: gpt-5.4-2026-03-05; API key from the
+# matching provider env var, e.g. OPENAI_API_KEY; or pass --api-base/--api-key)
+pip install openai tenacity
+export OPENAI_API_KEY="your-api-key-here"
+python evaluate/score_rubric.py --input your_rubric.jsonl
+```
+
+Both scorers join your answers against the ground truth in `metadata/` by
+`qa_id` — no reference answers needed in your submission.
+
+## License and Data Use
+
+AudioSpan is released for **non-commercial research and evaluation**. Our
+artifacts (QA items, rubric criteria, anchor manifests, sound events, and
+evaluation scripts) are under CC BY-NC-SA 4.0; copyright of the source
+recordings remains with their original creators. The recordings are
+included only for evaluation, and downloading the dataset constitutes
+agreement not to redistribute the audio or use it in commercial products.
+
+## Citation
+
+If you find AudioSpan useful for your research, please consider citing:
+
+```bibtex
+@article{huang2026audiospan,
+  title   = {AudioSpan: Spanning the Duration and Depth of Audio Comprehension},
+  author  = {Huang, Wen and Chu, Yunfei and Gao, Meng and He, Haolin and Xu, Jin},
+  journal = {arXiv preprint arXiv:ARXIV_ID},
+  year    = {2026}
+}
+```